Diffusion magnetic resonance imaging (dMRI) utilizes the sensitivity of the dMRI signals to the Brownian motion of water molecules to probe brain microstructure at the cellular level. By modeling relations between the signals and tissue microstructure, the existing diffusion methods extract various local tissue properties from dMRI signals, providing indirect but unique and relevant information of cellular structures. Long-range fiber pathways can also be tracked with tractography algorithms, to investigate the structural connectivity. Nowadays, dMRI is playing a vital role in clinical practice for evaluating structural integrity in multiple diseases, such as stroke, epilepsy, brain tumors, and neurodegenerative disorders, as well as in neuroscience studies to track structure alterations, such as in brain developmental and aging processes.
Following its great success, the dMRI has become a core element in many world-wide brain projects. A large amount of dMRI datasets have been collected and accumulated in various brain research projects for various aims. Lots of these data used advanced protocols with the multishell and high angular acquisition, and many of them were collected from different research or clinical centers. New methodologies are under development to fully explore and utilize the values of these data. These data, however, suffer from significant inter-site variability due to both hardware and sequence-related factors and there is an increasing need for novel acquisition and modeling strategies. Advanced analytics approaches, especially in combination with deep-learning techniques, have opened new opportunities for accelerated acquisition and improved model fitting. These exciting progress have inspired the call for this research topic, and we are particularly interested in how these new methodologies in dMRI can advance our understanding of brain structures and brain diseases.
The sub-themes include but are not limited to the following:
1) Novel acquisition and modeling of dMRI to further improve the accuracy of microstructure imaging.
2) Novel methods for the multi-center dMRI, to control the cross-center variation.
3) Machine-learning techniques that facilitate the dMRI acquisition and modeling processes.
4) Potential applications of dMRI in brain development or diseases, such as traumatic brain injury, neurosurgical planning, and neurodegenerative diseases.
5) Novel techniques or software for processing and visualization of dMRI.
Diffusion magnetic resonance imaging (dMRI) utilizes the sensitivity of the dMRI signals to the Brownian motion of water molecules to probe brain microstructure at the cellular level. By modeling relations between the signals and tissue microstructure, the existing diffusion methods extract various local tissue properties from dMRI signals, providing indirect but unique and relevant information of cellular structures. Long-range fiber pathways can also be tracked with tractography algorithms, to investigate the structural connectivity. Nowadays, dMRI is playing a vital role in clinical practice for evaluating structural integrity in multiple diseases, such as stroke, epilepsy, brain tumors, and neurodegenerative disorders, as well as in neuroscience studies to track structure alterations, such as in brain developmental and aging processes.
Following its great success, the dMRI has become a core element in many world-wide brain projects. A large amount of dMRI datasets have been collected and accumulated in various brain research projects for various aims. Lots of these data used advanced protocols with the multishell and high angular acquisition, and many of them were collected from different research or clinical centers. New methodologies are under development to fully explore and utilize the values of these data. These data, however, suffer from significant inter-site variability due to both hardware and sequence-related factors and there is an increasing need for novel acquisition and modeling strategies. Advanced analytics approaches, especially in combination with deep-learning techniques, have opened new opportunities for accelerated acquisition and improved model fitting. These exciting progress have inspired the call for this research topic, and we are particularly interested in how these new methodologies in dMRI can advance our understanding of brain structures and brain diseases.
The sub-themes include but are not limited to the following:
1) Novel acquisition and modeling of dMRI to further improve the accuracy of microstructure imaging.
2) Novel methods for the multi-center dMRI, to control the cross-center variation.
3) Machine-learning techniques that facilitate the dMRI acquisition and modeling processes.
4) Potential applications of dMRI in brain development or diseases, such as traumatic brain injury, neurosurgical planning, and neurodegenerative diseases.
5) Novel techniques or software for processing and visualization of dMRI.
